Matrix Factorizations: SVD and PCA

This lecture covers matrix factorizations, specifically Singular Value Decomposition (SVD) and Principal Component Analysis (PCA). It explores how these methods are used in machine learning to find latent features in data, such as in the context of the Netflix prize competition. The instructor discusses the motivation behind these techniques and their applications in predicting user ratings for movies based on existing ratings. The lecture also delves into the mathematical formulations of SVD and PCA, highlighting their role in explaining ratings through numerical representations of items and users.